Causal Retrieval with Semantic Consideration

ArXi:2504.04700v2 Announce Type: replace Recent advancements in large language models (LLMs) have significantly enhanced the performance of conversational AI systems. To extend their capabilities to knowledge-intensive domains such as biomedical and legal fields, where the accuracy is critical, LLMs are often combined with information retrieval (IR) systems to generate responses based on retrieved documents. However, for IR systems to effectively such applications, they must go beyond simple semantic matching and accurately capture diverse query intents, including causal relationships.